Abstract：Considering that the metallurgical mechanism model can provide reliable quality variables during BOF blowing, but not present their quantitative relationship. The influence factors on end-point carbon of steel were analyzed by mechanism model and partial leastsquares method. Then based on fuzzyreasoning neural network system, a prediction model of endpoint carbon for converter was established. The results show that this method can effectively enhance the hit rate of endpoint carbon prediction and the training speed of network. The hit rate reaches 94.12% in absolute error range of ±0.02% and 56.86% in relative error range of ±10%.
刘冬梅，王淑阁，赵成林，邹宗树，余艾冰. 基于新变量选择方法的FNN预报转炉终点[J]. 中国冶金, 2007, 17(2): 34-34.
LIU Dongmei,WANG Shuge,ZHAO Chenglin,ZOU Zongshu,YU Aibing. FuzzyReasoning Neural Network System Based on a New VariableSelecting Method to Predicate EndPoint of BOF. China Metallurgy, 2007, 17(2): 34-34.
